Abstract
The two key factors to design an ensemble of neural networks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on Stacked Generalization as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of Stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on Stacked Generalization are better than classical combiners.
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Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M. (2006). Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_19
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DOI: https://doi.org/10.1007/11829898_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
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